Vertaile menetelmiä

	Cramérin V	Khii toiseen tunnusluvun riippumattomuustesti	Logistinen regressio
Tieteenala	Tilastotiede	Tilastotiede	Tutkimuksen tilastomenetelmät
Menetelmäperhe	Hypothesis test	Hypothesis test	Process / pipeline
Syntyvuosi	1946	1900	1958
Kehittäjä	Harald Cramér	Karl Pearson	David Roxbee Cox
Tyyppi	Nonparametric association measure	Nonparametric test of association	Method
Alkuperäislähde	Cramér, H. (1946). Mathematical Methods of Statistics. Princeton University Press. ISBN: 978-0691080420	Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302), 157–175.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Rinnakkaisnimet	cramers v, cramer v, phi coefficient (r×c), Cramer's V (İlişki Kuvveti)	chi-squared test, Pearson's chi-square test, test of independence, ki-kare bağımsızlık testi	logit model, binomial logistic regression, LR
Liittyvät	3	2	3
Tiivistelmä	Cramer's V is a nonparametric effect-size statistic that measures the strength of association between two categorical variables on a scale from 0 to 1. Introduced by the Swedish mathematician Harald Cramér in his 1946 work Mathematical Methods of Statistics, it generalises the phi coefficient to tables of any size, making it the standard companion statistic to the chi-square test.	The chi-square test of independence is a nonparametric hypothesis test that examines whether two categorical variables are associated by comparing observed and expected frequencies in a cross-tabulation. It rests on the chi-square criterion introduced by Karl Pearson in 1900.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.